license: apache-2.0
base_model:
- meta-llama/Llama-3.2-3B-Instruct
library_name: adapter-transformers
tags:
- chemistry
- biology
- legal
- code
- medical
- finance
Nidum-Llama-3.2-3B-Uncensored
Welcome to Nidum!
At Nidum, we believe in pushing the boundaries of innovation by providing advanced and unrestricted AI models for every application. Dive into our world of possibilities and experience the freedom of Nidum-Llama-3.2-3B-Uncensored, tailored to meet diverse needs with exceptional performance.
Explore Nidum's Open-Source Projects on GitHub: https://github.com/NidumAI-Inc
Key Features
- Uncensored Responses: Capable of addressing any query without content restrictions, offering detailed and uninhibited answers.
- Versatility: Excels in diverse use cases, from complex technical queries to engaging casual conversations.
- Advanced Contextual Understanding: Draws from an expansive knowledge base for accurate and context-aware outputs.
- Extended Context Handling: Optimized for handling long-context interactions for improved continuity and depth.
- Customizability: Adaptable to specific tasks and user preferences through fine-tuning.
Use Cases
- Open-Ended Q&A
- Creative Writing and Ideation
- Research Assistance
- Educational Queries
- Casual Conversations
- Mathematical Problem Solving
- Long-Context Dialogues
How to Use
To start using Nidum-Llama-3.2-3B-Uncensored, follow the sample code below:
import torch
from transformers import pipeline
pipe = pipeline(
"text-generation",
model="nidum/Nidum-Llama-3.2-3B-Uncensored",
model_kwargs={"torch_dtype": torch.bfloat16},
device="cuda", # replace with "mps" to run on a Mac device
)
messages = [
{"role": "user", "content": "Tell me something fascinating."},
]
outputs = pipe(messages, max_new_tokens=256)
assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
print(assistant_response)
Datasets and Fine-Tuning
The following fine-tuning datasets are leveraged to enhance specific model capabilities:
- Uncensored Data: Enables unrestricted and uninhibited responses.
- RAG-Based Fine-Tuning: Optimizes retrieval-augmented generation for knowledge-intensive tasks.
- Long Context Fine-Tuning: Enhances the model's ability to process and maintain coherence in extended conversations.
- Math-Instruct Data: Specially curated for precise and contextually accurate mathematical reasoning.
Benchmarks
After fine-tuning with uncensored data, Nidum-Llama-3.2-3B demonstrates superior performance compared to the original LLaMA model, particularly in accuracy and handling diverse, unrestricted scenarios.
Benchmark Summary Table
| Benchmark | Metric | LLaMA 3B | Nidum 3B | Observation |
|---|---|---|---|---|
| GPQA | Exact Match (Flexible) | 0.3 | 0.5 | Nidum 3B demonstrates significant improvement, particularly in generative tasks. |
| Accuracy | 0.4 | 0.5 | Consistent improvement, especially in zero-shot scenarios. | |
| HellaSwag | Accuracy | 0.3 | 0.4 | Better performance in common sense reasoning tasks. |
| Normalized Accuracy | 0.3 | 0.4 | Enhanced ability to understand and predict context in sentence completion. | |
| Normalized Accuracy (Stderr) | 0.15275 | 0.1633 | Slightly improved consistency in normalized accuracy. | |
| Accuracy (Stderr) | 0.15275 | 0.1633 | Shows robustness in reasoning accuracy compared to LLaMA 3B. |
Insights:
- GPQA Results: Fine-tuning on uncensored data has boosted Nidum 3B's Exact Match and Accuracy, particularly excelling in generative and zero-shot tasks involving domain-specific knowledge.
- HellaSwag Results: Nidum 3B consistently outperforms LLaMA 3B in common sense reasoning benchmarks, indicating enhanced contextual and semantic understanding.
Contributing
We welcome contributions to improve and extend the model’s capabilities. Stay tuned for updates on how to contribute.
Contact
For inquiries, collaborations, or further information, please reach out to us at info@nidum.ai.
Explore the Possibilities
Dive into unrestricted creativity and innovation with Nidum-Llama-3.2-3B-Uncensored!